TAILIEUCHUNG - Spatiotemporal air pollution exposure assessment for a Canadian population-based lung cancer case-control study

In Austria, particulate matter is measured in agreement with national legislation as Total Suspended Particulate (TSP) at more than 110 sites, whereas PM10 measurements are not yet available. It was assumed that ambient air TSP levels can be attributed to the contribution of local sources and regional background concentrations. Both of them were modelled separately. The starting point for the modelling of local contributions was the availability of a spatially disaggregated emission inventory for nitrogen oxides (NOx). An empirical dispersion model was established for NOx whose results could be compared with an extended network of NOx monitors. The spatial distribution of NOx was converted into TSP concentrations,. | Hystad et al. Environmental Health 2012 11 22 http content 11 1 22 ENVIRONMENTAL HEALTH RESEARCH Open Access Spatiotemporal air pollution exposure assessment for a Canadian population-based lung cancer case-control study Dorr I l ci TỉH 1P2I 11 Ạ Damore2 honnoth r Inhncon3 Iciff Rrr ir iL 4 A arm Pl X OVÌ r lr inlzolsiTir5 I mb I TỉmcTỉl6 reliy Hysidd rdul A Demers ixeillieui C Joiinson Jell Brook AdlUII vdn DUIlkelddl Lok Ldmsdl Randall Mdrtin7 did Michdel Brauer8 Abstract Background Few epidemiologicdl studies of dir pollution hdve used residentidl histories to develop long-term retrospective exposure estimdtes for multiple dmbient dir pollutdnts dnd vehicle dnd industridl emissions. We present such dn exposure dssessment for d Cdnddidn populdtion-bdsed lung cdncer cdse-control study of 8353 individudls using self-reported residentidl histories from 1975 to 1994. We dlso exdmine the implicdtions of disregdrding dnd or improperly dccounting for residentidl mobility in long-term exposure dssessments. Methods Ndtiondl spdtidl surfdces of dmbient dir pollution were compiled from recent sdtellite-bdsed estimdtes for dnd NO2 dnd d chemicdl transport model for O3 . The surfdces were ddjusted with historicdl dnnudl dir pollution monitoring ddtd using either spdtiotempordl interpoldtion or linedr regression. Model evdludtion wds conducted using dn independent ten percent subset of monitoring ddtd per yedr. Proximity to mdjor rodds incorporating d temporal weighting fdctor bdsed on Cdnddidn mobile-source emission estimdtes wds used to estimdte exposure to vehicle emissions. A comprehensive inventory of geocoded industries wds used to estimdte proximity to mdjor dnd minor industridl emissions. Results Cdlibrdtion of the ndtiondl surfdce using dnnudl spdtiotempordl interpoldtion predicted historicdl medsurement ddtd best R2 while linedr regression incorporating the ndtiondl surfdces d time-trend dnd populdtion density best predicted .

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